The Integration of Web3 and AI: Building the Next Generation of Internet Infrastructure

Web3, as a decentralized, open, and transparent new internet model, has a natural opportunity for integration with AI. Under the traditional centralized architecture, AI computing and data resources are subject to strict limitations, facing multiple challenges such as computing power bottlenecks, privacy breaches, and algorithm opacity. In contrast, Web3, based on distributed technology, can provide new momentum for the development of AI through shared computing power networks, open data markets, and privacy computing. At the same time, AI can also bring numerous enhancements to Web3, such as smart contract optimization and anti-cheating algorithms, facilitating the development of its ecosystem. Therefore, exploring the combination of Web3 and AI is of great significance for building the next-generation internet infrastructure and unlocking the value of data and computing power.

Data-Driven: The Solid Foundation of AI and Web3

Data is the core driving force behind the development of AI, just like fuel is to an engine. AI models need to digest a large amount of high-quality data in order to gain deep understanding and strong reasoning abilities. Data not only provides the training foundation for machine learning models but also determines the accuracy and reliability of the models.

The traditional centralized AI data acquisition and utilization model has the following main issues:

• The cost of data acquisition is high, making it difficult for small and medium-sized enterprises to bear.
• Data resources are monopolized by large technology companies, creating data islands.
• Personal data privacy is at risk of leakage and abuse.

Web3 can address the pain points of traditional models with a new decentralized data paradigm:

• Collecting network data in a decentralized manner, cleaning and transforming it to provide real and high-quality data for AI model training.
• Adopt the "label to earn" model, incentivizing global workers through tokens to participate in data annotation, gathering global expertise to enhance data analysis capabilities.
• The blockchain data trading platform provides a public and transparent trading environment for both data supply and demand sides, incentivizing data innovation and sharing.

Nevertheless, there are some issues with data acquisition in the real world, such as varying data quality, high processing difficulty, and insufficient diversity and representativeness. Synthetic data may be a highlight of the Web3 data track in the future. Based on generative AI technology and simulation, synthetic data can mimic the properties of real data, serving as an effective supplement to real data and improving data utilization efficiency. In fields such as autonomous driving, financial market trading, and game development, synthetic data has already shown its mature application potential.

Privacy Protection: The Role of FHE in Web3

In the data-driven era, privacy protection has become a global focus. The introduction of regulations such as the EU's General Data Protection Regulation (GDPR) reflects a strict safeguarding of personal privacy. However, this has also posed challenges: some sensitive data cannot be fully utilized due to privacy risks, which undoubtedly limits the potential and reasoning capabilities of AI models.

FHE, or Fully Homomorphic Encryption, allows for computation operations to be performed directly on encrypted data without the need to decrypt it, and the results of the computations are consistent with those obtained by performing the same calculations on plaintext data.

FHE provides solid protection for AI privacy computing, allowing GPU computing power to perform model training and inference tasks in an environment without touching the original data. This brings huge advantages to AI companies. They can safely open API services while protecting trade secrets.

FHEML supports encrypted processing of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing the risk of data leakage. In this way, FHEML strengthens data privacy and provides a secure computing framework for AI applications.

FHEML is a supplement to ZKML, where ZKML proves the correct execution of machine learning, while FHEML emphasizes computing on encrypted data to maintain data privacy.

Power Revolution: AI Computing in Decentralized Networks

The computational complexity of current AI systems doubles every three months, resulting in a surge in computing power demand that far exceeds the supply of existing computational resources. For example, training a large language model requires immense computing power, equivalent to 355 years of training time on a single device. This shortage of computing power not only limits the advancement of AI technology but also makes these advanced AI models unattainable for most researchers and developers.

At the same time, the global utilization rate of GPUs is below 40%, coupled with a slowdown in the performance improvement of microprocessors and a chip shortage caused by supply chain and geopolitical factors, which has exacerbated the computing power supply issue. AI practitioners are caught in a dilemma: either purchase hardware or rent cloud resources, and they urgently need a demand-driven, cost-effective computing service.

The decentralized AI computing power network aggregates idle GPU resources from around the world, providing AI companies with a computing power market that is both economical and easily accessible. Demand-side computing power can publish computational tasks on the network, and smart contracts assign tasks to miner nodes that contribute computing power. Miners execute the tasks and submit the results, receiving point rewards after verification. This solution improves resource utilization efficiency and helps to address the computing power bottleneck issues in fields such as AI.

In addition to the general decentralized computing networks, there are also dedicated computing networks focused on AI training and inference.

The decentralized computing power network provides a fair and transparent computing power market, breaking monopolies, lowering application thresholds, and improving the efficiency of computing power utilization. In the web3 ecosystem, decentralized computing power networks will play a key role in attracting more innovative dapps to join and jointly promote the development and application of AI technology.

DePIN: Web3 Empowers Edge AI

Imagine this: your smartphone, smart watch, and even smart devices in your home all have the capability to run AI - this is the charm of Edge AI. It allows computation to occur at the source of data generation, achieving low latency and real-time processing, while also protecting user privacy. Edge AI technology has already been applied in key areas such as autonomous driving.

In the Web3 space, we have a more familiar name - DePIN. Web3 emphasizes decentralization and the sovereignty of user data. DePIN enhances user privacy protection and reduces the risk of data leakage by processing data locally. The native token economic mechanism of Web3 can incentivize DePIN nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is developing rapidly in certain public chain ecosystems, becoming one of the preferred platforms for project deployment. High TPS, low transaction fees, and technological innovation provide strong support for DePIN projects. Currently, the market value of some DePIN projects on public chains has exceeded $10 billion, and several well-known projects have made significant progress.

IMO: New Paradigm for AI Model Release

The concept of IMO was first proposed by a certain protocol to tokenize AI models.

In the traditional model, due to the lack of a revenue-sharing mechanism, once an AI model is developed and launched in the market, developers often find it difficult to earn continuous revenue from the subsequent use of the model. This is especially true when the model is integrated into other products and services, making it hard for the original creators to track usage, let alone earn revenue from it. Additionally, the performance and effectiveness of AI models often lack transparency, making it difficult for potential investors and users to assess their true value, which limits the market recognition and commercial potential of the models.

IMO provides a new funding support and value-sharing method for open-source AI models, allowing investors to purchase IMO tokens and share the profits generated by the models in the future. A specific protocol uses a particular ERC standard, combined with AI oracles and OPML technology to ensure the authenticity of the AI models and that token holders can share in the profits.

The IMO model enhances transparency and trust, encourages open-source collaboration, adapts to trends in the cryptocurrency market, and injects momentum into the sustainable development of AI technology. The IMO is currently in the early trial phase, but as market acceptance increases and the scope of participation expands, its innovation and potential value are worth looking forward to.

AI Agent: A New Era of Interactive Experience

AI Agents can perceive their environment, think independently, and take appropriate actions to achieve set goals. Supported by large language models, AI Agents can not only understand natural language but also plan decisions and execute complex tasks. They can serve as virtual assistants, learning users' preferences through interaction and providing personalized solutions. Even without explicit instructions, AI Agents can autonomously solve problems, improve efficiency, and create new value.

A certain AI-native application platform provides a comprehensive and user-friendly suite of creation tools, supporting users to configure robot functions, appearance, voice, and connect to external knowledge bases, dedicated to building a fair and open AI content ecosystem. Utilizing generative AI technology, it empowers individuals to become super creators. The platform has trained a specialized large language model to make role-playing more human-like; voice cloning technology can accelerate the personalized interaction of AI products, reducing voice synthesis costs by 99%, with voice cloning achievable in just 1 minute. The AI Agent customized by this platform can currently be applied in various fields such as video chatting, language learning, and image generation.

In the integration of Web3 and AI, the current focus is more on exploring the infrastructure layer, including key issues such as how to obtain high-quality data, protect data privacy, host models on the chain, improve the efficient use of decentralized computing power, and validate large language models. As these infrastructures gradually improve, we have reason to believe that the integration of Web3 and AI will give birth to a series of innovative business models and services.